Two-Photon\nAbsorption in Fluorescent Protein Chromophores:\nTDDFT and CC2 Results
Bibliographic record
Abstract
Two-photon spectroscopy of fluorescent\nproteins is a powerful bioimaging\ntool. Considerable effort has been made to measure absolute two-photon\nabsorption (TPA) for the available fluorescent proteins. Being a technically\ninvolved procedure, there is significant variation in the published\nexperimental measurements even for the same protein. In this work,\nwe present a time-dependent density functional theory (TDDFT) study\non isolated chromophores comparing the ability of four functionals\n(PBE0, B3LYP, CAM-B3LYP, and LC-BLYP) combined with the 6-31+G(d,p)\nbasis set to reproduce averaged experimental TPA energies and cross\nsections. The TDDFT energies and TPA cross sections are also compared\nto corresponding CC2/6-31+G(d,p) results for excitation to S<sub>1</sub> for the five smallest chromophores. In general, the computed TPA\nenergies are less functional dependent than the TPA cross sections.\nThe variation between functionals is more pronounced when higher-energy\ntransitions are studied. Changes to the conformation of a chromophore\nare shown to change the TPA cross-section considerably. This adds\nto the difficulty of comparing an isolated chromophore to the one\nembedded in the protein environment. All functionals considered give\nmoderate agreement with the corresponding CC2 results; in general,\nthe TPA cross sections determined by TDDFT are 1.5–10 times\nsmaller than the corresponding CC2 values for excitation to S<sub>1</sub>. LC-BLYP and CAM-B3LYP give erroneously large TPA cross sections\nin the higher-energy regions. On the other hand, B3LYP and PBE0 yield\nvalues that are of the same order of magnitude and in some cases very\nclose to the averaged experimental data. Thus, based on the results\nreported here, B3LYP and PBE0 are the preferred functionals for screening\nchromphores for TPA. However, at best, TDDFT can be used to semiquantitatively\nscan chromophores for potential TPA probes and highlight spectroscopic\npeaks that could be present in the mature protein.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".